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In rotary machines, the bearing failure is one of the major causes of the breakdown of machinery. The bearing degradation monitoring is a great anxiety for the prevention of bearing failures. This paper aims to present a combination of the stationary wavelet decomposition and extra-trees regression (ETR) for the evaluation of bearing degradation.

The higher order cumulants features are extracted from the bearing vibration signals by using the stationary wavelet decomposition (stationary wavelet transform [SWT]). The extracted features are then subjected to the ETR for obtaining normal and failure state. A dominance level curve build using the dissimilarity data of test object and retained as health degradation indicator for the evaluation of bearing health.

Experiment conducts to verify and assess the effectiveness of ETR for the evaluation of performance of bearing degradation. To justify the preeminence of recommended approach, it is compared with the performance of random forest regression and multi-layer perceptron regression.

The experimental results indicated that the presently adopted method shows better performance for detecting the degradation more accurately at early stage. Furthermore, the diagnostics and prognostics have been getting much attention in the field of vibration, and it plays a significant role to avoid accidents.

Looks at the eighth published year of the ITCRR and the research, from far and near, involved in this. Muses on the fact that, though all the usual processes are to the fore, the downside part of the industry is garment making which is the least developed side. Posits that the manufacture of clothing needs to become more technologically advanced as does retailing. Closes by emphasising support for the community in all its efforts.

The aim of this paper is to provide some specific information on the effects of DC voltage stress on the current, rise time (Tr) and fall time (Tf), at switching between on and off state of power n‐MOSFET devices.

A constant positive electrical stress voltage technique is used to study the devices in this work by giving the gate a positively bias with respect to source and a short circuit of the drain with the grounded source. Voltage stress is gradually increased by automatic 1 V step until it reaches the max tolerated value by the gate dielectric (70 V for device studied in this paper). Response of the device for electrical stress was measured for different doses (stress time).

The experimental results show that the rise time increases the beginning of stress dose and then it almost stabilises with time, while fall time decreases at first and then starts to increase for higher stress time. The modification of the device switching time parameters were associated to positive oxide charge and interface state Si/SiO₂ effects.

This paper offers new information concerning a very important field in microelectronic devices where the switching speed of the components becomes a major requirement. The technique used to improve the device speed has a very low cost and a simple feasibility.

The purpose of this paper is to analyze the dependent competing failure reliability of harmonic drive (HD) with strength failure and degradation failure.

Based on life tests and stiffness degradation experiments, Wiener process is used to establish the accelerated performance degradation model of HD. Model parameter distribution is estimated by Bayesian inference and Markov Chain Monte Carlo (MCMC) and stiffness degradation failure samples are obtained by a three-step sampling method. Combined with strength failure samples of HD, copula function is used to describe the dependence between strength failure and stiffness degradation failure.

Strength failure occurred earlier than degradation failure under high level accelerated condition; degradation failure occurred earlier than strength failure under medium- or low-level accelerated condition. Gumbel copula is the optimum copula function for dependence modeling of strength failure and stiffness degradation failure. Dependent competing failure reliability of HD is larger than independent competing failure reliability.

The reliability evaluation method of dependent competing failure of HD with strength failure and degradation failure is first proposed. Performance degradation experiments during accelerated life test (ALT), step-down ALT and life test under rated condition are conducted for Wiener process based step-down accelerated performance degradation modeling.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

Hospital structures serve to protect and improve public health; however, they are recognized as a major source of environmental degradation. Thus, an effective performance evaluation framework is required to improve hospital sustainability. In this context, this study presents a holistic methodology that integrates the sustainability balanced scorecard (SBSC) with fuzzy Delphi method and fuzzy multi-criteria decision-making approaches for evaluating the sustainability performance of hospitals.

Initially, a comprehensive list of relevant sustainability evaluation criteria was considered based on six SBSC-based dimensions, in line with triple-bottom-line sustainability dimensions, and derived from the literature review and experts’ opinions. Then, the weights of perspectives and their respective criteria are computed and ranked utilizing the fuzzy analytic hierarchy process. Subsequently, the hospitals’ sustainable performance values are ranked based on these criteria using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution.

A numerical application was conducted in six public hospitals to exhibit the proposed model’s applicability. The results of this study revealed that “Patient satisfaction,” “Efficiency,” “Effectiveness,” “Access to care” and “Waste production,” respectively, are the five most important criteria of sustainable performance.

The new model will provide decision-makers with management tools that may help them identify the relevant factors for upgrading the level of sustainability in their hospitals and thus improve public health and community well-being.

This is the first study that proposes a new hybrid decision-making methodology for evaluating and comparing hospitals’ sustainability performance under a fuzzy environment.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

The purpose of this study is to evaluate the potentials of nano-additives in enhancement of oxidation and thermal stability of biolubricants thereby, improving the resistance of dispersed nanolubricants to thermal degradation under elevated temperature.

This study evaluates the oxidation stability and tribological performance of nano-enhanced biolubricants. Graphene and maghemite nanoparticles at 0.1% volume concentration were dispersed into coconut oil. Oxidation stability was analysed using a thermal analyser to understand the effect of nano-additives on thermal degradation of lubricants under increasing temperature. In addition, tribological performance and viscosity of the tested lubricants were evaluated using a four-ball friction tester and viscometer according to American Society for Testing and Materials standards.

The results reveal that the oxidation stability of biolubricants dispersed with nano-additives improves due to delayed thermal degradation. The nano-enhanced biolubricants’ oxidation onset temperature was delayed by 18.75 °C and 37.5 °C, respectively, for maghemite (MGCO) and graphene (XGCO) nanolubricants. This improvement imparts the performance viscosity and tribological performance positively. For graphene-enhanced nanolubricant, 10.4% and 5.6% were reduced, respectively, in coefficient of friction (COF)and wear scar diameter (WSD), whereas 3.43% and 4.3% reduction in COF and WSD, respectively, for maghemite-enhanced nanolubricant compared with coconut oil. The viscosity index of nanolubricants was augmented by 7.36% and 13.85%, respectively, for maghemite and graphene nanolubricants.

The excellent performance of nanolubricants makes them suitable candidate as sustainable lubricants for machining with regard to environmental benefits and energy saving.

The effect of graphene and maghemite nanoparticles on the oxidation stability and tribological performance of biolubricants has been investigated. It is an original work and yet to be published elsewhere.

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

The purpose of this paper is to evaluate and analyze lube oil performance. The plant experience shows that the oil, even after completion of a number of hours of recommended operation, has some residual life. It is desirable to maximize its use to conserve this scarce resource but avoid failures. At present, continuation or change of the oil is decided, based on the manufacturer's recommendation and experience in the plant. The suggested oil change period is conservative and results in non‐efficient usage of oil. This practice needs refinement to include all possible properties/attributes of oil and use of appropriate procedure to assess its realistic performance.

Parameter profile approach (PPA) is applied to evaluate and analyze the lubricating oil performance parameters.

Physical and chemical properties related to degradation of the lube oil are considered as lubricating oil performance parameters. The value of these performance parameters from time to time, obtained by analyzing samples drawn from the system, are analyzed through PPA. In this approach, mean, median, performance parameter index and time performance index are defined and evaluated at different time intervals. The suggested procedure is illustrated by means of an example.

The suggested procedure will be helpful for the maintenance personnel in planned oil replacement, and in identifying the weak oil properties in respect of the identified lube oil performance parameters.